MLS-C01 Modeling Practice Question
Which THREE evaluation metrics are appropriate for a multi-class classification problem? (Choose 3.)
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Confusion matrix.
Confusion matrix (A) is appropriate because it provides per-class performance metrics (TP, FP, FN, TN) for each class, which is essential for multi-class evaluation. Accuracy (B) is appropriate as it measures overall correctness across all classes, a common and intuitive metric for multi-class problems. F1 score with macro or micro averaging (E) is appropriate because macro averaging computes F1 per class and averages them equally, while micro averaging aggregates contributions across all classes, both suitable for multi-class. Mean squared error (C) is incorrect; it is a regression metric not used for classification. Precision-recall curve (D) is typically used for binary classification, not standard for multi-class without extensions.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Confusion matrix.
Why this is correct
Confusion matrix provides per-class performance.
- ✓
Accuracy.
Why this is correct
Accuracy is a straightforward metric for multi-class.
- ✗
Mean squared error.
Why it's wrong here
MSE is for regression problems.
- ✗
Precision-recall curve.
Why it's wrong here
PR curve is typically used for binary classification.
- ✓
F1 score (macro/micro).
Why this is correct
F1 score can be averaged across classes.
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